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Found 3 papers, 1 papers with code
HETAL: Efficient Privacy-preserving Transfer Learning with Homomorphic Encryption
Although numerous previous studies proposed to use homomorphic encryption to resolve the data privacy issue in transfer learning in the machine learning as a service setting, most of them only focused on encrypted inference.
Collecting and Analyzing Multidimensional Data with Local Differential Privacy
Motivated by this, we first propose novel LDP mechanisms for collecting a numeric attribute, whose accuracy is at least no worse (and usually better) than existing solutions in terms of worst-case noise variance.
Collecting and Analyzing Data from Smart Device Users with Local Differential Privacy
Organizations with a large user base, such as Samsung and Google, can potentially benefit from collecting and mining users' data.
Databases
